Information processing device, information processing method, and non-transitory computer readable storage medium

ABSTRACT

An information processing device according to the present application includes an extraction unit and a subsequent stage generation unit. The extraction unit extracts a last conversation of a feedback utterance estimated to indicate a predetermined reaction of a second utterance subject relative to an utterance made by a first utterance subject, from a set of a plurality of conversations, based on a score assigned to the feedback utterance. The subsequent stage generation unit generates a subsequent stage classifier for deriving an index indicating a category of an unknown conversation, based on the last conversation extracted by the extraction unit.

CROSS-REFERENCE TO RELATED APPLICATION(S)

The present application claims priority to and incorporates by referencethe entire contents of Japanese Patent Application No. 2017-171926 filedin Japan on Sep. 7, 2017.

BACKGROUND OF THE INVENTION 1. Field of the Invention

This disclosure relates to an information processing device, aninformation processing method, and a non-transitory computer readablestorage medium.

2. Description of the Related Art

Conventionally, a device that registers in advance, words used when auser asks the person he or she is talking to to repeat or clarify whatthe person has just said in a conversation and the like, and thatdetermines that the conversation is not effectively carried out when theregistered word is included in the conversation, has been developed (seeJapanese Laid-open Patent Publication No. 2007-43356).

However, the device described above sometimes cannot make adetermination on words other than the words registered in advance,because the device depends on the words registered in advance todetermine whether conversation is effectively carried out.

SUMMARY OF THE INVENTION

According to one innovative aspect of the subject matter described inthis disclosure, an information processing device includes: (i) anextraction unit that extracts a last conversation of a feedbackutterance estimated to indicate a predetermined reaction of a secondutterance subject relative to an utterance made by a first utterancesubject, from a set of a plurality of conversations, based on a scoreassigned to the feedback utterance; and (ii) a subsequent stagegeneration unit that generates a subsequent stage classifier forderiving an index indicating a category of an unknown conversation,based on the last conversation extracted by the extraction unit.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating a part of a configuration of aninformation processing system 1;

FIG. 2 is a diagram illustrating an example of the contents of a labelto be applied to a feedback utterance;

FIG. 3 is a diagram illustrating examples of the contents of labels tobe applied to the last conversation of the feedback utterance;

FIG. 4 is a diagram illustrating an example of feedback utterances inputto an utterance classifier 24 and utterance scores output by theutterance classifier 24;

FIG. 5 is a diagram illustrating another configuration of theinformation processing system 1;

FIG. 6 is a diagram illustrating an example of feedback utterancesbiased to the first category side or the second category side;

FIG. 7 is a diagram illustrating examples of conversations stored in aconversation learning data storage device 52;

FIG. 8 is a diagram schematically illustrating a learning process;

FIG. 9 is a diagram illustrating an example of a conversation input to aconversation classifier 66 and information output from the conversationclassifier 66;

FIG. 10 is a flowchart illustrating a processing flow for generating theutterance classifier 24 by the information processing system 1;

FIG. 11 is a flowchart illustrating a processing flow for generating theconversation classifier 66 by the information processing system 1;

FIG. 12 is a diagram illustrating functional configurations of a firstcomparative example and a second comparative example;

FIG. 13 is a diagram illustrating an example of processing results ofthe information processing system 1, the first comparative example, andthe second comparative example;

FIG. 14 is a diagram illustrating an example of a functionalconfiguration of an information processing system 1A of a firstmodification;

FIG. 15 is a diagram illustrating an example of a functionalconfiguration of an information processing system 1B of a secondmodification;

FIG. 16 is a diagram illustrating an example of a functionalconfiguration of an information processing system 1C of a thirdmodification; and

FIG. 17 is a diagram illustrating an example of processing results ofthe information processing system 1, the first modification, the secondmodification, and the third modification.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Hereinafter, an embodiment of an information processing device, aninformation processing method, and a non-transitory computer readablestorage medium of the present invention will be described in detail withreference to the accompanying drawings. In the following explanation, aword issued by an automatic answering machine or a user is referred toas an “utterance”, a set of a plurality of the utterances is referred toas “conversation”, and an utterance estimated to indicate apredetermined reaction of a second utterance subject relative to anutterance made by a first utterance subject is referred to as a“feedback utterance”. An example of the first utterance subject is theautomatic answering machine, and an example of the second utterancesubject is the user (person).

The information processing device is implemented by one or moreprocessors. For example, the information processing device derives anindex indicating a category of conversation, on the conversation betweenthe user and the automatic answering machine. For example, the categoryof conversation is whether the conversation is unnatural (conversely,whether the conversation is natural). For example, when the conversationis unnatural, the conversation is not established because an appropriateresponse is not automatically being made. The category of conversationis not limited to whether the conversation is unnatural, and may beoptionally defined.

Moreover, the information processing device generates an utteranceclassifier and a conversation classifier while performing the process.The utterance classifier derives an index (utterance score, which willbe described below) that is applied to the feedback utterance and thatindicates the probability of an unnatural conversation or a naturalconversation being made immediately before the feedback utterance. Inthe embodiment to be described below, the utterance score is an indexindicating the probability of an unnatural conversation being madeimmediately before the feedback utterance. Moreover, in the following,the “conversation made immediately before” (or “last conversation”) isregarded as a combination of the utterance of the user and the utterancemade by the automatic answering machine relative to the utterance of theuser. The conversation classifier derives an index (conversation score,which will be described below) that is applied to a conversation andthat indicates the probability of the conversation being unnatural.

Configuration

FIG. 1 is a diagram illustrating a part of a configuration of aninformation processing system 1. For example, the information processingsystem 1 includes a conversation log storage device 10, a feedbackutterance storage device 12, an utterance learning data storage device14, an acquisition unit 20, an utterance classifier generation unit(prior stage generation unit) 22, and an utterance classifier 24. Thefunctional configurations described above can also be configured asdevices.

For example, the acquisition unit 20, the utterance classifiergeneration unit 22, and the utterance classifier 24 are implemented whena hardware processor such as a central processing unit (CPU) executes acomputer program (software). Moreover, a part or all of these componentsmay be implemented by hardware (including circuitry) such as a largescale integration (LSI), an application specific integrated circuit(ASIC), a field-programmable gate array (FPGA), and a graphicsprocessing unit (GPU), or may be implemented by cooperation of softwareand hardware.

For example, each of the storage devices included in the informationprocessing system 1 is implemented by a read only memory (ROM), a harddisk drive (HDD), a flash memory, a secure digital (SD) card, a randomaccess memory (RAM), a register, and the like.

The conversation log storage device 10 stores therein log information ofa conversation. For example, the log information of a conversation istext information of a conversation made between an automatic answeringmachine operated by artificial intelligence (AI) and a user. The textinformation may be obtained by converting uttered voice through voicerecognition.

The feedback utterance storage device 12 stores therein feedbackutterances. The feedback utterances are extracted from the conversationlog storage device 10 to be stored in the feedback utterance storagedevice 12. For example, each of the feedback utterances is a feedbackutterance set in advance. For example, a feedback utterance is extracted(acquired) when an operator extracts the feedback utterance from loginformation of a conversation stored in the conversation log storagedevice 10, or when a predetermined device (or another system)automatically extracts the feedback utterance from log information of aconversation stored in the conversation log storage device 10, on thebasis of the words in the feedback utterance set in advance.

As described above, the feedback utterance storage device 12 storestherein the feedback utterance acquired from the conversation logstorage device 10. FIG. 2 is a diagram illustrating examples of thefeedback utterance. For example, the feedback utterance includes anutterance such as (1) “No, no. What are you implying?” and (2) “Got it.Thanks”. It is not possible to accurately determine whether the lastconversation of the feedback utterance is established, if the feedbackutterance is only denying the last conversation or accepting the lastconversation. Consequently, the information processing system 1 of thepresent embodiment generates the utterance classifier 24 capable ofoutputting a score (probability) indicating whether the lastconversation is established or the like, only by the feedback utterance.

First, a conversation with a feedback utterance is acquired from theconversation log storage device 10, and as illustrated in FIG. 3, anatural label (label indicating a first category) or an unnatural label(label indicating a second category) is applied to the last conversationof the feedback utterance. Learning data in which the label applied tothe last conversation of the feedback utterance is set as a teacherlabel of the feedback utterance is generated, and is stored in theutterance learning data storage device 14.

In the example of FIG. 3, learning data in which a teacher label ofbeing natural is applied to the feedback utterance of “Thank you”because the last conversation of “Thank you” is natural, and a teacherlabel of being unnatural is applied to the feedback utterance of “Whatdo you mean?” because the last conversation of “What do you mean?” isunnatural, is generated.

The utterance classifier generation unit 22 generates the utteranceclassifier 24 by learning the learning data described above. Theutterance classifier generation unit 22 also performs learning by usinga method such as a deep learning technique using the neural network andthe like, and a support vector machine (SVM).

Upon receiving a known or unknown feedback utterance, the utteranceclassifier 24 derives an utterance score indicating the probability ofthe conversation made immediately before the feedback utterance beingunnatural. For example, the feedback utterance received by the utteranceclassifier 24 is the feedback utterance acquired by the acquisition unit20 from the feedback utterance storage device 12. The utterance score isan index indicating the probability of the utterance made by theautomatic answering machine immediately before the feedback utterancebeing unnatural, relative to the utterance made by a person immediatelybefore the utterance. In other words, with an increase in the utterancescore, the probability of the last conversation of the feedbackutterance between the user and the automatic answering machine beingunnatural increases.

FIG. 4 is a diagram illustrating an example of feedback utterances inputto the utterance classifier 24 and utterance scores output by theutterance classifier 24. For example, the utterance score derived forthe utterance “No, no. What are you implying? (FB1 in the diagram)” ishigher than the utterance score derived for the utterance “I like yourhonest answer (FB2 in the diagram)”.

By using the score derived by the utterance classifier 24, it ispossible to determine whether the conversation is natural or unnatural,only by the feedback utterance and without closely examining thecontents of the conversation. Moreover, in the present embodiment, abinary label of a natural label indicating that the last conversation isnatural, or an unnatural label indicating that the last conversation isunnatural is applied to the feedback utterance. The label indicating thefirst category or the label indicating the second category can beapplied to any feedback utterance, regardless of whether the lastconversation is established (or natural) or unestablished (orunnatural). For example, the label indicating the first category may beapplied to a feedback utterance indicating praise, acceptance,understanding, thankfulness, interest, or the like, and the labelindicating the second category may be applied to a feedback utteranceindicating disappointment, communication failure, incomprehension,despise, boredom, or the like.

The utterance classifier 24 stores correspondence information obtainedby associating the feedback utterance with the utterance score, in ascored feedback utterance storage device 50, which will be describedbelow, of the information processing system 1.

FIG. 5 is a diagram illustrating another configuration of theinformation processing system 1. In addition to the configurationillustrated in FIG. 1, the information processing system 1 furtherincludes a conversation log storage device 40, the scored feedbackutterance storage device 50, a conversation learning data storage device52, an extraction unit 62, a learning data generation unit 63, aconversation classifier generation unit (subsequent stage generationunit) 64, and a conversation classifier 66. These functionalconfigurations can also be configured as devices. Any of the functionalconfigurations included in the information processing system 1 may alsobe configured as a device.

For example, a part or all of the extraction unit 62, the learning datageneration unit 63, the conversation classifier generation unit 64, andthe conversation classifier 66 can be implemented when a hardwareprocessor such as the CPU executes a computer program (software). A partor all of these components may also be implemented by hardware(including circuitry) such as the LSI, the ASIC, the FPGA, and the GPU,or may be implemented by cooperation of software and hardware.

For example, the components illustrated in FIG. 1 and FIG. 5 communicatethrough inter-software communication or a hardware network. For example,the hardware network may include a wide area network (WAN), a local areanetwork (LAN), the Internet, a dedicated circuit, a radio base station,a provider, and the like.

For example, the conversation log storage device 40 stores therein loginformation of a conversation. The log information of the conversationmay be the same as or different from the information stored in theconversation log storage device 10.

The scored feedback utterance storage device 50 stores therein feedbackutterances with utterance scores derived by the utterance classifier 24,and the utterance scores of the feedback utterances.

The extraction unit 62 acquires a feedback utterance and a scorecorresponding to the feedback utterance from the scored feedbackutterance storage device 50, and acquires a conversation with thefeedback utterance (feedback utterance and the last conversation) fromthe conversation log storage device 40. When the conversation with thefeedback utterance is acquired from the conversation log storage device40, the feedback utterance extracted from the scored feedback utterancestorage device 50 will be used.

The extraction unit 62 acquires a conversation with the feedbackutterance extracted from the conversation log storage device 40, andassigns a score to the last conversation of the feedback utterance onthe basis of the score assigned to the feedback utterance extracted fromthe scored feedback utterance storage device 50.

To assign a score to a conversation with a feedback utterance not storedin the scored feedback utterance storage device 50, a score is acquiredby supplying the feedback utterance of the conversation to the utteranceclassifier 24.

The learning data generation unit 63 generates learning data in whichthe last conversation of the feedback utterance and the category are setas a teacher label, by applying a label indicating the category to thelast conversation of the feedback utterance, on the basis of the scoreassigned to the last conversation of the feedback utterance. Thelearning data generation unit 63 then stores the learning data in theconversation learning data storage device 52.

For example, a first category label is applied to the last conversationof the feedback utterance to which a score equal to or less than a firstthreshold (for example, 0.3) is assigned, and a second category label isapplied to the last conversation of the feedback utterance to which ascore equal to or more than a second threshold (for example, 0.7) isassigned. Instead of using the threshold of the score as describedabove, the feedback utterances may be sorted in the descending order ofthe scores, and the second category label may be applied to the lastconversation of the feedback utterance in a certain high ratio (forexample, 20%), and the first category label may be applied to the lastconversation of the feedback utterance not in the certain high ratio.

FIG. 6 is a diagram illustrating an example of feedback utterances eachassigned with a score. For example, when the first category label isapplied to the feedback utterance assigned with a score equal to or lessthan 0.3, and the second category label is applied to the feedbackutterance assigned with a score equal to or more than 0.7, an example ofthe feedback utterance with the utterance score biased to the firstcategory side is “You are clever”, and “I like your honest answer”, andan example of the feedback utterance with the utterance score biased tothe second category side is “No, no. What are you implying?” and “Thisconversation is going nowhere”.

FIG. 7 is a diagram illustrating examples of conversations extractedfrom the conversation log storage device 40. A label is applied to thelast conversation of the feedback utterance “No, no. What are youimplying?”, on the basis of the score of the feedback utterance “No, no.What are you implying?”. A label is applied to the last conversation ofthe feedback utterance “You are clever”, on the basis of the score ofthe feedback utterance “You are clever”. For example, when the firstcategory label is applied to the last conversation of the feedbackutterance to which the score equal to or less than 0.3 is assigned, andthe second category label is applied to the last conversation of thefeedback utterance to which the score equal to or more than 0.7 isassigned, learning data in which the first category label is applied tothe conversation of “What is 426+129?” and “The answer is 555”, and thesecond category label is applied to the conversation of “Let me see thebrowsing history” and “Ha-ha-ha”, is generated.

The conversation learning data storage device 52 stores therein learningdata in which the label indicating the category described above (forexample, the first category or the second category) is applied to thelast conversation of the feedback utterance (not including the feedbackutterance) generated by the learning data generation unit 63.

The conversation classifier generation unit 64 generates theconversation classifier 66 for deriving a conversation score that is anindex indicating the category of an unknown conversation, on the basisof the last conversation of the feedback utterance extracted by theextraction unit 62. The conversation classifier generation unit 64performs learning on the basis of the last conversation of the feedbackutterance biased to the first category side, the last conversation ofthe feedback utterance biased to the second category side, andinformation on the label (first category or second category) applied toeach of the last conversations. For example, the learning is carried outby machine learning. The conversation classifier generation unit 64generates the conversation classifier 66 on the basis of the machinelearning in which the last conversation extracted by the extraction unit62 and information on the label applied to the last conversation are setas a teacher label. The conversation classifier generation unit 64 mayalso learn the above by using methods such as a deep learning techniqueusing the neural network and the SVM.

FIG. 8 is a diagram schematically illustrating a learning process. Forexample, when the feedback utterance biased to the second category sideis “No, no. What are you implying?”, conversations a to c madeimmediately before “No, no. What are you implying?” will be extracted.Moreover, when the feedback utterance biased to the first category sideis “You are clever”, conversations d to f made immediately before “Youare clever” will be extracted. In this manner, the conversationclassifier 66 is learned from the conversation having a high probabilityof being a natural or unnatural conversation, in the conversationsbetween the user and the automatic answering machine.

Upon receiving an unknown or known conversation, the conversationclassifier 66 derives a conversation score (subsequent stage index)indicating the category of the conversation. The conversation score isan index indicating the probability of the utterance made by theautomatic answering device being unnatural, relative to an utterancemade by a person immediately before the utterance. In other words, withan increase in the utterance score, the probability of the conversationmade between the user and the automatic answering machine beingunnatural increases.

FIG. 9 is a diagram illustrating an example of an (unknown) conversationinput to the conversation classifier 66 and information output by theconversation classifier 66. For example, when the utterance of the user“I can't win pachinko” and the utterance of the automatic answeringmachine “How about making a donation?” that is the response to theutterance of the user are input to the conversation classifier 66, theconversation classifier 66 outputs that the probability of theconversation described above being unnatural is 95 percent. In thismanner, the conversation classifier 66 can also determine thenaturalness or unnaturalness of an unknown conversation that is notfollowed by the feedback utterance.

Moreover, in the process described above, it is assumed that theprobability of a conversation A of “Let me see the browsing history” and“Ha-ha-ha” has been learned to be highly unnatural. For example, when anunknown conversation of “Let me see the history” and “Ha-ha-ha” is inputto the conversation classifier 66, the conversation classifier 66derives a conversation score having a high probability of beingunnatural for the unknown conversation, as for the conversation A. Thisis because the words “browsing history” and “history” are close inmeaning.

Process for Generating Utterance Classifier

FIG. 10 is a flowchart illustrating a processing flow for generating theutterance classifier 24 by the information processing system 1. First,the utterance classifier generation unit 22 acquires learning dataincluding a feedback utterance and a teacher label applied to thefeedback utterance, from the utterance learning data storage device 14(S100).

Next, the utterance classifier generation unit 22 learns the probabilityof an unnatural conversation or a natural conversation being madeimmediately before the feedback utterance, on the basis of the learningdata acquired at S100 (S102). Next, the utterance classifier generationunit 22 generates the utterance classifier 24 on the basis of thelearning result at S102 (S104).

Next, the acquisition unit 20 acquires the feedback utterance to assignan utterance score, and inputs the acquired feedback utterance to theutterance classifier 24. The utterance classifier 24 assigns anutterance score to the input feedback utterance, and storescorrespondence information in which the utterance score of the feedbackutterance and the feedback utterance are associated with each other, inthe scored feedback utterance storage device 50 of the informationprocessing system 1 (S106). In this manner, the process of the presentflowchart is terminated.

With the process described above, the utterance classifier 24 thatderives an utterance score indicating the probability of the lastconversation being unnatural relative to the feedback utterance isgenerated, and a score is assigned to a predetermined feedback utteranceby the generated utterance classifier 24.

Process for Generating Conversation Classifier

FIG. 11 is a flowchart illustrating a processing flow for generating theconversation classifier 66 by the information processing system 1.First, the extraction unit 62 acquires correspondence information storedin the scored feedback utterance storage device 50 (S200). Next, theextraction unit 62 automatically extracts the feedback utteranceassigned with the utterance score, from the correspondence informationacquired at S200 (S202).

Next, the extraction unit 62 extracts a conversation with each feedbackutterance (feedback utterance and the last conversation of the feedbackutterance) extracted at S202, from the log information stored in theconversation log storage device 40. The extraction unit 62 then assignsa score to the last conversation of the extracted feedback utterance onthe basis of the score assigned to the feedback utterance extracted fromthe scored feedback utterance storage device 50 (S204). Next, thelearning data generation unit 63 generates learning data includinginformation in which the last conversation of the feedback utterance andthe category are a teacher label, by applying a label indicating thecategory to the last conversation of the feedback utterance on the basisof the score assigned at step S204. The learning data generation unit 63then stores the learning data in the conversation learning data storagedevice 52 (S206).

Next, the conversation classifier generation unit 64 performs learningon the basis of the learning data that is generated at S206 and that isstored in the conversation learning data storage device 52 (S208). Theconversation classifier generation unit 64 then generates theconversation classifier 66 on the basis of the learning result at S208(S210). In this manner, the process of the present flowchart isterminated.

With the process described above, the conversation classifier 66 thatderives the conversation score for indicating the unnaturalness of theconversation will be generated.

In the example illustrated above, the process of generating theutterance classifier 24 and the process of generating the conversationclassifier 66 are different processes. However, these processes may behandled as a series of processes.

CONCLUSION

The last conversation of the feedback utterance indicating the firstcategory may be unnatural, and the last conversation of the feedbackutterance indicating the second category may be natural. Whether theconversation between the automatic answering device and the user isnatural or unnatural is not always indicated by the category of thefeedback utterance and may involve other factors. For example, when theautomatic answering machine utters a kind word to the user, the user maymake a feedback utterance indicating the first category. Alternatively,for example, when the automatic answering machine utters a word to makethe user angry, the user may make a feedback utterance indicating thesecond category. Consequently, it is not appropriate to simply determinethat the last conversation of the feedback utterance indicating thefirst category is natural, and the last conversation of the feedbackutterance indicating the second category is unnatural.

Moreover, in the conversation, the feedback utterance indicating thefirst category or the second category is not frequently made.Consequently, when a machine learning technique is not applied to theconversation applied with a label, it has been difficult to determinethe naturalness or unnaturalness of the conversation not followed by thefeedback utterance in a wide range.

On the contrary, the information processing system 1 of the presentembodiment generates the conversation classifier 66 by performingmachine learning on the last conversation of the scored feedbackutterance that is extracted from the log information of theconversation. Thus, compared with the method of simply identifying thelast conversation of the feedback utterance indicating the firstcategory as a natural conversation and the last conversation of thefeedback utterance indicating the second category as an unnaturalconversation, it is possible to determine the naturalness orunnaturalness of the conversation in a wide range. Consequently, thisconversation classifier 66 can improve the coverage of the conversationto be determined, and also determine the naturalness or unnaturalness ofthe conversation for an unknown conversation.

Moreover, the information processing system 1 of the present embodimentgenerates the conversation classifier 66 by performing machine learningon the last conversation of the feedback utterance having the utterancescore biased to the first category side or the second category side.Consequently, the conversation classifier 66 can more accuratelydetermine whether the conversation is natural or unnatural.

Furthermore, the information processing system 1 of the presentembodiment can easily generate the conversation classifier 66 suitablefor a task or domain. For example, to generate the conversationclassifier 66 suitable for a task or domain in a system of a comparativeexample, log information on the conversation made in the task or domainis collected, and a label is applied to the collected conversations. Thesystem of the comparative example then generates the conversationclassifier 66 by performing machine learning on the conversation appliedwith the label. In this case, the conversation classifier 66 needs to becreated manually for each task or domain each time, thereby increasingthe cost.

On the other hand, in the information processing system 1 of the presentembodiment, when the utterance classifier 24 is generated on the basisof the log information of a certain conversation, the utteranceclassifier 24 can be applied to various tasks or domains. Thus, it ispossible to easily generate the conversation classifier 66. For example,in the information processing system 1, the conversation classifier 66is generated by extracting the last conversation of the feedbackutterance assigned with the utterance score from the log information ofthe conversation made in the target task or domain, and by performingmachine learning on the extracted conversation and the utterance score.Consequently, it is possible to generate the conversation classifier 66suitable for the target task or domain. In this manner, the informationprocessing system 1 can generate the conversation classifier 66 byapplying the utterance classifier 24, even if a label is not applied tothe conversation made in the target task or domain. In other words, withthe method of the present embodiment, when a database of scored feedbackutterances is once created, it is possible to automatically learn theconversation classifier 66 from the dialogue log of the task or domainand the scored feedback utterance storage device 50 without trouble, inother words, at a low cost, even if a new task or domain is to betackled.

In the embodiment described above, the conversation classifier 66derives the index indicating the unnaturalness of the conversation.However, the “unnaturalness” may be replaced with another feature. Forexample, an index indicating the probability of the last conversation ofthe feedback utterance being a predetermined category may be derived.For example, an index indicating the probability of the lastconversation of the feedback utterance being beneficial to the user, anindex indicating the probability of the last conversation of thefeedback utterance being a conversation to improve the feeling of theuser, and the like may be derived. In such a case, a label correspondingto the type of index is applied to the feedback utterance, instead ofthe label indicating the first category or the label indicating thesecond category. Moreover, a label corresponding to the type of index isapplied to the last conversation of the feedback utterance, instead ofthe natural label or the unnatural label.

In the embodiment described above, the conversation classifier 66derives the probability of a conversation belonging to one of the twocategories (for example, the second category). Alternatively, theconversation classifier 66 can derive the probability of a conversationbelonging to one of three or more categories. In this case, for example,labels indicating the categories of three or more types of conversationswill be prepared. For example, it is assumed that a label indicating athird category of a neutral conversation is prepared in addition to thelabels indicating the first category and the second category. In thiscase, the label indicating one of the first category to the thirdcategory is assigned to the last conversation of the feedback utterancestored in the utterance learning data storage device 14. The informationprocessing system 1 then learns a relation between one of the firstcategory to third category and the feedback utterance. Moreover, forexample, the information processing system 1 automatically extracts thefeedback utterance having a score included in a range indicating anatural conversation, an unnatural conversation, and a neutralconversation to which the utterance score is set in advance, from thecorrespondence information. The information processing system 1 thengenerates the conversation classifier 66, by learning a relation betweenthe last conversation of the extracted feedback utterance and the labelindicating the category of the conversation.

First and Second Comparative Examples

FIG. 12 is a diagram illustrating functional configurations of a firstcomparative example and a second comparative example. The firstcomparative example illustrated in the upper diagram in FIG. 12 is amethod based on supervised learning using manually created data. In thefirst comparative example, a learning unit 100 machine learns theinformation stored in the utterance learning data storage device 14, anda conversation classifier 102 is generated from the learned result. Theinformation stored in the utterance learning data storage device 14 isthe last conversation of the feedback utterance applied with a naturallabel or an unnatural label.

In the second comparative example illustrated in the lower diagram inFIG. 12, a score is assigned to the conversation depending on which ofthe feedback utterance indicating the first category and the feedbackutterance indicating the second category follows more. In the secondcomparative example, the utterance classifier 24 and the conversationclassifier 66 are not used.

In the second comparative example, a score derivation unit 110 derives ascore for the conversation, on the basis of the information (feedbackutterance not assigned with a score) stored in the feedback utterancestorage device 12 and the log information stored in the conversation logstorage device 40. For example, Score is derived by the followingformula (1). NEG is the number of feedback utterances indicating thesecond category that follows the target conversation of the loginformation. POS is the number of feedback utterances indicating thefirst category that follows the target conversation of the loginformation.

Score=NEG−POS  (1)

Comparison Between First Comparative Example and Second ComparisonExample

FIG. 13 is a diagram illustrating an example of processing results ofthe information processing system 1, the first comparative example, andthe second comparative example. The vertical axis of the diagramindicates the matching rate, and the horizontal axis indicates thereproduction rate. The matching rate is an index indicating theprobability of a correct answer (unnatural conversation) being includedin the result determined as an unnatural conversation by the informationprocessing system 1. In this case, the information processing system 1determines that the conversation is unnatural when the conversationscore is equal to or more than a threshold. A correct (unnaturalconversation) label is applied by a person. The reproduction rate is anindex indicating the probability of the correct answer being determinedas an unnatural conversation by the information processing system 1. Anarea under the curve (AUC) is an area of a portion below the curve inthe graph.

As illustrated in the diagram, the performance of the informationprocessing system 1 of the present embodiment is equivalent to that ofthe first comparative example, or equal to or higher than that of thefirst comparative example. More specifically, the method of thecomparative example is an expensive method because the learning data ofthe conversation classifier needs to be manually created for each taskor domain. However, the performance of the method of the presentembodiment is equivalent to that of the first comparative example, eventhough the method is inexpensive and does not depend on the task ordomain. Moreover, compared with the second comparative example in whichthe feedback utterance is vague and the feedback utterance is notfrequently made, the information processing system 1 of the presentembodiment has a significant performance.

Hereinafter, an information processing system 1A of a firstmodification, an information processing system 1B of a secondmodification, and an information processing system 1C of a thirdmodification that are modifications of the information processing system1 will be described.

First Modification

The first modification is an example in which the conversationclassifier generation unit 64 further learns the conversation betweenthe automatic answering machine and the user that is stored in theutterance learning data storage device 14 and that is applied with anatural label or an unnatural label. FIG. 14 is a diagram illustratingan example of a functional configuration of the information processingsystem 1A of the first modification.

Second Modification

The second modification is an example in which the utterance classifier24 is omitted. In this case, the information processing system 1Bincludes the feedback utterance storage device 12 instead of the scoredfeedback utterance storage device 50. FIG. 15 is a diagram illustratingan example of a functional configuration of the information processingsystem 1B of the second modification. The conversation classifiergeneration unit 64 of the information processing system 1B derives aconversation candidate having a high probability of being a naturalconversation and a conversation candidate having a high probability ofbeing an unnatural conversation using the formula (1) described above.

For example, the information processing system 1B sets a conversation inwhich the score is within a predetermined range as the conversationcandidate having a high probability of being a natural conversation, anda conversation in which the score is within a range different from thepredetermined range as the conversation candidate having a highprobability of being an unnatural conversation.

Third Modification

FIG. 16 is a diagram illustrating an example of a functionalconfiguration of the information processing system 1C of the thirdmodification. In the third modification, the learning data generationunit 63 and the conversation learning data storage device 52 areomitted, and a score derivation unit 120 is included instead of theconversation classifier 66 of the information processing system 1. Amongthe feedback utterances stored in the scored feedback utterance storagedevice 50, the extraction unit 62 extracts the feedback utterance with ascore within a first range (for example, within 20% or 30% from theminimum value) and a score within a second range (for example, within20% or 30% from the maximum value). The score derivation unit 120derives a score by using the feedback utterance extracted by theextraction unit 62. More specifically, the score derivation unit 120derives a score by using the formula (1) described above.

Comparison with Modifications

FIG. 17 is a diagram illustrating an example of processing results ofthe information processing system 1, the first modification, the secondmodification, and the third modification. The same explanation as FIG.13 is omitted.

As illustrated in FIG. 17, the performances of the informationprocessing system 1, the first modification, and the secondmodifications are higher than that of the third modification notincluding the conversation classifier 66. The performances of theinformation processing system 1 and the first modification are higherthan that of the second modification not including the utteranceclassifier 24. In other words, the experiment has demonstrated that theconversation classifier 66 contributes significantly to the performanceof the information processing system 1 of the present embodiment. It isto be noted that the performance of the first modification is littlehigher than that of the information processing system 1.

With the embodiment described above, the information processing system 1can automatically acquire a clue for determining whether an unknownconversation is in a predetermined category, by including the extractionunit 62 that extracts the last conversation of the feedback utteranceestimated to indicate a predetermined reaction of the second utterancesubject relative to the utterance made by the first utterance subject,from a set of conversations, on the basis of the utterance scoreassigned to the feedback utterance; and the conversation classifiergeneration unit 64 that generates the conversation classifier 66 forderiving an index indicating the category of the unknown conversation,on the basis of the last conversation extracted by the extraction unit62.

In one aspect of the present invention, it is possible to automaticallyacquire a clue for determining whether an unknown conversation is in apredetermined category.

Although the invention has been described with respect to specificembodiments for a complete and clear disclosure, the appended claims arenot to be thus limited but are to be construed as embodying allmodifications and alternative constructions that may occur to oneskilled in the art that fairly fall within the basic teaching herein setforth.

What is claimed is:
 1. An information processing device, comprising: anextraction unit that extracts a last conversation of a feedbackutterance estimated to indicate a predetermined reaction of a secondutterance subject relative to an utterance made by a first utterancesubject, from a set of a plurality of conversations, based on a scoreassigned to the feedback utterance; and a subsequent stage generationunit that generates a subsequent stage classifier for deriving an indexindicating a category of an unknown conversation, based on the lastconversation extracted by the extraction unit.
 2. The informationprocessing device according to claim 1, wherein the subsequent stagegeneration unit further generates the subsequent stage classifier thatderives the index indicating the category of the unknown conversation,based on a label applied to the last conversation.
 3. The informationprocessing device according to claim 2, wherein the subsequent stagegeneration unit generates the subsequent stage classifier by performingmachine learning using information on the last conversation extracted bythe extraction unit and the label applied to the last conversation aslearning data.
 4. The information processing device according to claim1, further comprising: the subsequent stage classifier generated by thesubsequent stage generation unit, wherein the subsequent stageclassifier derives an index indicating a category of an unknownconversation.
 5. The information processing device according to claim 1,further comprising a prior stage classifier that assigns a score to thefeedback utterance.
 6. The information processing device according toclaim 5, further comprising a prior stage generation unit that generatesthe prior stage classifier.
 7. The information processing deviceaccording to claim 1, wherein the extraction unit extracts a lastconversation of a feedback utterance assigned with a score biased to afirst category side or a second category side from the set ofconversations.
 8. An information processing device, comprising: anacquisition unit that acquires a feedback utterance estimated toindicate a predetermined reaction of a second utterance subject relativeto an utterance made by a first utterance subject; and a prior stagegeneration unit that generates a prior stage classifier for deriving anindex indicating a probability of a conversation of a predeterminedcategory being made immediately before the feedback utterance acquiredby the acquisition unit.
 9. The information processing device accordingto claim 8, further comprising the prior stage classifier generated bythe prior stage generation unit.
 10. The information processing deviceaccording to claim 9, wherein the acquisition unit acquires the feedbackutterance, a last conversation of the feedback utterance applied with afirst category, and a last conversation of the feedback utteranceapplied with a second category, and the prior stage generation unitgenerates the prior stage classifier based on information acquired bythe acquisition unit.
 11. The information processing device according toclaim 10, wherein the prior stage generation unit generates the priorstage classifier by performing machine learning using the feedbackutterance acquired by the acquisition unit and a label indicating acategory applied to the last conversation as learning data.
 12. Aninformation processing method causing a computer to execute: extractinga last conversation of a feedback utterance estimated to indicate apredetermined reaction of a second utterance subject relative to anutterance made by a first utterance subject, from a set of a pluralityof conversations, based on a score assigned to the feedback utterance;and generating a subsequent stage classifier that derives an indexindicating a category of an unknown conversation, based on the extractedlast conversation.
 13. A non-transitory computer readable storage mediumhaving stored therein a computer program that causes a computer toexecute: extracting a last conversation of a feedback utteranceestimated to indicate a predetermined reaction of a second utterancesubject relative to an utterance made by a first utterance subject, froma set of a plurality of conversations, based on a score assigned to thefeedback utterance; and generating a subsequent stage classifier thatderives an index indicating a category of an unknown conversation, basedon the extracted last conversation.
 14. An information processing methodcausing a computer to execute: acquiring a feedback utterance estimatedto indicate a predetermined reaction of a second utterance subjectrelative to an utterance made by a first utterance subject; andgenerating a prior stage classifier that derives an index indicating aprobability of a conversation of a predetermined category being madeimmediately before the acquired feedback utterance.
 15. A non-transitorycomputer readable storage medium having stored therein a computerprogram that causes a computer to execute: acquiring a feedbackutterance estimated to indicate a predetermined reaction of a secondutterance subject relative to an utterance made by a first utterancesubject; and generating a prior stage classifier that derives an indexindicating a probability of a conversation of a predetermined categorybeing made immediately before the acquired feedback utterance.